Personalized media stations

ABSTRACT

Systems, methods, and non-transitory computer-readable storage media for generating an internet radio media station based on metadata available on the user&#39;s media library. The media station can be generated in response to a subscription request to an internet radio service. In one example, the media station is generated without a user seed. Metadata related to the user&#39;s media library is analyzed and format rules are selected and configured according to the analysis. The format rules are associated with slots in a media station that define the playback sequence of the media station.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional application No.61/717,598 filed on Oct. 23, 2012, which is expressly incorporated byreference herein in its entirety.

TECHNICAL FIELD

The present technology pertains to algorithmically created media stationprogramming, and more specifically pertains to algorithmically createdmedia station programming by online media distribution services.

BACKGROUND

Like many other processes, the programming of media stations has becomeincreasingly reliant on algorithms for selecting and scheduling content.For example, terrestrial radio use qualitative market research andquantitative analytical research to select songs to play and when toplay them by taking into account parameters that define the mediastations—most importantly genre and demographic information. The resultscommonly dictate playing a tight rotation of the same 20-30 songs from apool of as few as 200-300 songs in rotation. Often these songs areselected from a short list of media items being promoted by one or morerecord labels.

Another common form of algorithmically created media station programmingincludes Internet radio stations such as PANDORA and IHEART RADIO, amongothers. PANDORA's media stations are typically characterized by mediaitems that have similar intrinsic musicological attributes to a seed orset of organizing principles governing items played on the mediastation. PANDORA utilizes a robust database of metadata attributesdescribing media items and selects media items that have similarmetadata attributes.

While popular, PANDORA's stations are prone to playing media that thelistener may not like or identify with. This is likely because PANDORApredominately takes into account only one or two types of data at most(i.e. similar types of metadata or musicological attributes) whencreating stations.

In addition to algorithmically created media station programmingservices, there are many websites that publish playlists of media itemscreated by human editors. However, these playlists are made without anyregard for the listening tastes of a user.

In an attempt to remedy the deficiencies in the art stemming from onlyusing one type of data, PANDORA now keeps track of media items that auser likes and doesn't like, and plays those media items lessfrequently, but this isn't a sufficient solution. Utilizing a diversearray of data types has proven difficult.

SUMMARY

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Disclosed are systems, methods, devices, and non-transitorycomputer-readable storage media for generating algorithmically createdmedia stations. Such media stations are created from a diverse array ofdata types including user profile data, media item metadata, similaritydata derived from collaborative filtering, commercial promotional data,and culturally informed editorial data. In some embodiments, userprofile data includes data derived from an online media store includingmedia item purchase data, and listening data.

As used herein a media station is defined as an algorithmically createdcollection of media items of which the selection and order of the mediaitems is exclusively determined by an algorithm. Each time a userengages with a media station the algorithm will uniquely select mediaitems for playback to the user. In some embodiments a user can skip amedia item played-back on the media station, but the listener otherwisehas no control over the order of media items presented. The term mediastation is used in contrast to “playlist” which is a published list ofmedia items, and for which the order and even inclusion of media itemsis commonly subject to some degree of manipulation by a user and/ormusic editor.

A media station can include format rules that define the content on themedia station. Each format rule can be used to identify media itemcandidates that can be played on the media station. For example, aprocessor can execute a format rule to identify one or more media itemsfrom a media item database. These media items can be candidates forplayback on the media station. In one embodiment, a media station caninclude an ordered list of slots where each slot is configured to storeone or more format rules. Playback of the media station can involveiterating through the list of slots. When playback advances to a givenslot, a format rule associated with the slot can be executed and a mediaitem from the media item database can be selected for playback. In someexamples, a collection of media items are identified from a format ruleand the media station can select a media item from the collection toplay. In some embodiments, the media item selected can vary depending onchanges to the media item database or attributes of the media items inthe database. In some embodiments a media item can be selected from acollection after a weighting process taking into account one or morefactors such as user preferences stored in a user profile to select themedia item. The slots can be sequenced to represent an order in whichformat rules are executed resulting in media items being selected forplayback on the media station in a unique sequence for each individualuser.

As content from a media station is presented to the listener, userfeedback on the presented media items can be provided by the listener.The user feedback can be processed to edit the media station andoptionally edit global parameters or relationships between media items.For example, the user feedback of media items that the listener dislikescan be banned from the media station. In some embodiments the userfeedback can be utilized as weighting criteria in selecting a media itemcandidate for a playback on a media station either on the currentlyplaying media station or in a future selection and listening session theuser may interact with. In some embodiments, relationships between mediaitems that the listener likes can be created if many listeners in thesystem provide similar feedback. In some examples, media items havingsimilar user feedback can be clustered and used to creategeneralizations about the listener's tastes and preferences. Theclustered feedback can also be used to identify other media items thatthe user may like.

The present technology includes a media station generation systemincluding a database of media item candidates. Some databases can bepopulated with potential media items based on an analysis of a user'spurchased and/or uploaded media library. In one example, a user's medialibrary can be analyzed and media items that are similar to media itemsin a user's library can be collected into a similarity database. Somedatabases can be populated with lists of media items deemed appropriateby an editor. Such editorial databases include media items appropriatefor a given situation, such as “working out,” or “quiet moods,” or“celebrations” while other editorial databases included media itemsappropriate for a particular genre, or meet some other criteria forinclusion in the database by an editor. Some databases can becommercially driven and include media items that are selected byalgorithms of an online store to target media items to a user based on aprediction to drive purchases of the song or album on which it appears.For example a database of music albums can be created that includesmusic albums for which a user already owns one or more songs from thatmedia item. This database can be used to program media stations thatencourage exposure to and purchase of the rest of the album dynamically.

In some embodiments, the system includes databases of media items thatcan be considered similar based on their intrinsic attributes. Suchdetermination can be made after analyzing media and representing them asvectors created based on metadata describing intrinsic characteristicssuch as genre, artist, origin, beat, tempo, mood or energy level, etc.After representing the media items as vectors, they can be organizedinto clusters of roughly similar media items and recorded in databasesof similar media items specifically to influence the programming of themedia stations.

In some embodiments extrinsic data from a collaborative filteringprocess can be utilized in the clustering process.

In some embodiments media items classified in a same or similar genrecan be clustered and the resulting clusters can be deemed to besub-genres. The distance between the sub-genres can be determined andsub-genres that are relatively close can be considered compatiblesub-genres, while sub-genres that are relatively far apart can beconsidered non-compatible sub-genres. In some embodiments the distancemeasurement is performed by mapping the clusters to a coordinate spaceand measuring a distance between them. This analysis can be used togenerate a list of sub-genres that go well together and can be includedin a list of “safe segue” sub-genres which indicates theircompatibility. The inverse list of “unsafe segues” can also be recordedand used to prohibit media items that are not compatible from beingplayed in the same media station.

In addition to media item candidate databases, the system can includedatabases of user preferences and observable trends on their store andradio station account. Such preferences could be represented in manyforms, but one example is in the form of feature vectors. A user'slibrary, or listening data can be used to generate a feature vector thatrepresents a user's listening taste. The vector can be predominatelyweighted towards one or more feature or characteristic and many vectorscould be used to represent different aspects of a user's preferences.However, candidates being considered for inclusion in a media stationcould be compared against such a vector. In some embodiments, the userpreferences in the user preference database can be used to weightcandidates using a scoring system.

The media station generation system further includes a stationgeneration module for creating a media station according to a list ofrules known as a programming model. In some embodiments the programmingmodel includes a plurality of slots, with each slot being assigned amedia selection rule to be used in selecting a media item to bepresented at that slot in the media station program. The media selectionrules can select media items from one or more of the databasesintroduced above.

In some embodiments, data can be shared between the online store, poolsof media items, and the station creation module to reflexively updateeach other. For example, as described above data from an online storecan be used to create a pool of media item candidates appropriate forthe user. Such pool can be used by the media station generation moduleto select media items for inclusion in a media station.

However, the media items presented to the user in the media station canbe communicated to the online store where they can be promoted for sale.Media items that are purchased or rated highly can then be used torefresh the pools of media items used by the media station generationmodule to create media stations.

Accordingly, the present technology provides an advanced andsophisticated blending of diverse data sources to generate high qualitymedia stations that are customized to a user's tastes. The system canfurther learn about a user's changing or evolving tastes throughmonitoring the user's interactions with the system and refine the mediastation and online store offerings over time.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates an exemplary media station generation system;

FIG. 2 illustrates an exemplary media station generation system;

FIG. 3 illustrates an exemplary clustering system;

FIG. 4 a illustrates an exemplary media station generation rule;

FIG. 4 b illustrates and exemplary output of a media station programmingformat;

FIG. 5 illustrates a system in which an online store, media items pools,and media station creation module reflexively influence each componentof the system;

FIG. 6 illustrates an exemplary media station user interface; and

FIGS. 7 a and 7 b illustrate an exemplary computing arrangement forexecuting programming modules of the disclosed technology.

DESCRIPTION

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.

As used herein the term “configured” shall be considered tointerchangeably be used to refer to configured and configurable, unlessthe term “configurable” is explicitly used to distinguish from“configured”. The proper understanding of the term will be apparent topersons of ordinary skill in the art in the context in which the term isused.

As used herein, the term “user” shall be considered to mean a user of anelectronic device(s). Actions performed by a user in the context ofcomputer software shall be considered to be actions taken by a user toprovide an input to the electronic device(s) to cause the electronicdevice to perform the steps embodied in computer software unlessexplicitly stated otherwise. In some instances a user can refer to auser account or profile associated with a particular electronic device.For example user preferences refer to data stored in association withina user account or profile that can reflect a user's real lifepreferences.

FIG. 1 illustrates an exemplary media station generation system. Theexemplary media station generation system 100 is configured to create amedia station based on a station generation rule 112 taking into accounta diverse array of user specific data, population influenced data,editorial data, promotional data, and media item characteristic data.The station generation rules can include rules for creating a stationbased on seed song, rules for creating a heuristically influencedstation, rules for creating an editorially formatted station (forexample, based on a genre or theme), rules for creating a stationtailored to a listener's preferences as derived from userpreferences/experiences heuristics and stored database 104, and rulesfor creating sponsored stations, etc.

The exemplary media station generation system 100 includes a stationcreation module 114 which creates media stations using one or more rulesfrom the station generation rules database 112 using media candidatesfrom databases 103, 104, 109, and 115.

The media similarity database 103 is a collection of media items deemedsimilar based on characteristics of the media items. As illustrated inFIG. 1 a collection of media items and any associated data or metadatain database 102 can be used by a feature based analysis module 111 toperform an analysis, by way of example a clustering analysis or localitysensitive hashing analysis, to determine other media items that aresimilar. The feature based analysis module 111 determines which mediaitems are potentially similar based on characteristics of the mediaitem. The output of such analysis is stored in the media similaritydatabase 103. In some embodiments the output is used to generate a listof candidate media items that are characteristically similar to one ormore other media items that may be used by the station creation module114 to create the media station. In some embodiments the output is usedto generate feature vector that can represent a representative mediaitem in a media item cluster, and station creation module 114 cancompare potential media item candidates for similarity with the featurevector when creating a media station.

Feature based analysis module 111 can be configured to cluster mediaitems into collections or groups based on the features of the mediaitems. Feature based analysis module can utilize media item informationand statistics about media item playback from the media items andstatistics database 101 and media item metadata from the media itemsmetadata database 102 to cluster the media items to determine mediaitems that are characteristically similar. For example, statistics frommedia items and statistics database 101 can include the frequency thatmedia items are presented in user's personal media libraries,relationships between purchases (e.g., many users who purchased thismedia item also purchased that media item), relationships between mediaitems (e.g., users often place this media item and that media itemtogether in the same playlist), and others. Media item metadata database102 can include metadata of media items such as genre, era, tempo,energy, and mood.

In some embodiments, the values for a property can include hierarchicalrelationships such that relationships can be created between media itemshaving different property values. For example, the era property value1980's can be hierarchically related to era property value representingeach individual year in the decade. As another example, the genreproperty value jazz can be hierarchically related to genre propertyvalues hard bop, cool jazz, free jazz, swing, jazz rock, soul jazz, andLatin Jazz.

Some embodiments of feature based clustering can include creating amulti-dimensional vector for each media item. The vector can beconfigured to represent the statistics and metadata related to aspecific media item. Each dimension of the vector can be associated witha feature or attribute of the media item. For example, one dimension canrepresent the popularity of the media item while another represents thetempo of the media item while another represents the genre of the mediaitem. Once a vector has been generated for each media item, the mediaitems can be clustered according to their respective vectors. In oneexample, vectors that are within a predetermined proximity of oneanother can be clustered together.

In some embodiments, vectors that are within a predetermined proximityof other vectors are clustered, and a feature vector representing theoverall cluster can be generated. In some embodiments, the featurevector can representative of a particular genre, era, tempo, or moodvalue if one of these characteristics is the predominate featureresulting in the grouping of media items into a cluster. Thus, theresulting cluster of vectors will contain media items that have asimilar property value. The collections or groups that result fromfeature based clustering by feature based analysis module 111 can bestored in media similarity database 103.

The population-based similarity database 109 is a collection of mediaitems which are deemed similar based on an analysis of engagement datafrom a population of users, also known as collaborative filtering.Population-based similarity database 109 is derived from an analysis bycollaborative filter 110 of data in the media items and statisticsdatabase 101 which includes the all-time purchase history of each user,the personal media library of each user (i.e., media the user haspurchased from the system plus media that the user personally owns), andother media related metadata associated with the user including userengagement history with media items. In some embodiments, this userengagement history can be reported to an online store by a media playingclient on a user's media playing device, personal computer, laptop,mobile device, etc, and stored in database 101.

Collaborative filter 110 can be configured to perform collaborativefiltering on information in database 101 to generate similarity database109. Collaborative filtering can include generating a similarity valuethat describes the similarity between two media items and storing thesimilarity value in similarity database 109. Collaborative filtering canalso include grouping similar media items into collections and storingthe collections in similarity database 109. Collaborative filtering canalso include applying pattern recognition or other heuristic techniquesto information available about users such as media related metadata togenerate taste and preference information. This can creategeneralizations as to what media items users commonly purchase together,similarities between the media items that are present in user's personalmedia libraries, similarities in user's personal libraries, media itemsthat are commonly not played together, and others. In some exampleswhere a collection of media items is created, the collection can berepresented as a feature vector.

In some embodiments, collaborative filter 110 analyzes data in database101 to determine which media items are statistically most likely toco-occur in users' media libraries. Items that co-occur in users' medialibraries can be said to be similar. More detail on this process can befound in application Ser. No. 12/646,916, filed on Dec. 23, 2009 whichis expressly incorporated by reference herein, in its entirety.

Candidates can also be selected from editorial candidate database 115which contains a collection media items that have been identified byeditors to go well with a specified genre. For example, editorial tagscan be useful to proxy play count data for new or under-exposed songs inthe database. Typically a new song has little airplay and little to nofeedback. Therefore the song has little visibility and is unlikely to beselected for playback. By attaching an editorial tag, the new song canbe promoted and gain traction from listeners. For example, a format rulecan be configured to select a media item having a particular editorialtag such as “new music: jazz.” Editorial tags can also be used toidentify picks by the editorial staff or to identify iconic media itemsof a specific genre that should be included in a media station focusingon that genre.

Additionally, user preference data 104, can be used to determinecandidates. User preference data can include the genre, mood, energy,era, or other media property associated with media items purchased, userrating, media items included in a playlist, recently played, orfrequently played, etc. For example, the taste user preference data caninclude a notice that the majority of media items purchased by a userhave been from the music genre jazz or have been from the 2010 era of anartist whose collection spans many decades. User preference data canalso include data on skips, or poor ratings of media items. In someembodiments, this data can be used to weight candidate media items.

User preference data can be derived from an analysis by preferencesheuristics module 120 of a variety of sources of data including a mediaitems and statistics database 101, and data regarding user feedback 108on media items experienced through media stations.

In some embodiments, in addition to the user preferences listed abovecan also include a determination that a user prefers to experience morenew media items as opposed to familiar media items, or vice versa. Thiscan be achieved by monitoring user experience data, and if a user isskipping or rating poorly a disproportionate number of new media items,but experiencing familiar media items, it can be assumed that the userprefers to experience to more hits, than discover new media items. Theinverse can also be true, in which it can be assumed that a user has aninterest in media item discovery. The fact that a user has an interestin media item discovery might also be inferred from more relaxedrequirements. Since it is not likely a user will rate every new mediaitem highly, it may be inferred that a user enjoys/prefers media itemdiscovery when a user doesn't skip many newly experienced media items,or rates a small portion of them highly. Another factor can be that auser purchases some of the newly experienced media items. In someembodiments, the system can experiment with a user to determine thispreference by playing more new media items in one experience and if thesystem does not observe many negative signals such as skips and bans ofmedia items, short experience duration it can be assumed the user has apreference for new media item discovery.

In some embodiments, a user preference can be determined from observinga user's actions in response to suggesting media items to purchase(either through explicit suggestion or implicit suggestion by exposingthe user to the media item through playback on a media station). Forexample, if a user were to purchase an album after being exposed toseveral songs on that album, this would be suggestive of a user'sinterest in this genre and artist, as well as the user's interest inowning albums as opposed to singles, which itself implies the user isinterested in hearing less popular tracks from at least some artists.

In some embodiments a seed media item 113 (or multiple seed media items)can be used to determine appropriate media item candidates for a mediastation by determining similar media items to the seed media item 113.In some embodiments, the seed can be a media item such as a song or canbe a characteristic of a media item such as an artist, album, genre,etc. A feature vector can be created based on the seed media item andsimilar media items can be selected by comparing the feature vector ofthe seed media item with similarity data 103 or 109. For example, one ormore media items having a vector similar to the feature vector of theseed media item can be selected. As another example, a collection ofmedia items can be selected when the feature vector of the seed mediaitem is similar (within a predetermined distance) to a feature vectorthat represents the collection of media items. In another embodiment,the seed media item can be an artist or genre instead of an actual mediaitem. The seed artist or genre can be processed by the media itemcandidate module 105 to create a media station. This can includeanalyzing the seed in view of user preferences 104 to determine one ormore media items from the user's personal media library that are canrepresent the media station. Media item candidates 105 for the mediastation can in turn be selected by comparing feature vectors of the oneor more representative media items to the vectors in similarity data103.

In some embodiments, media station generation rules 112 can be used bythe media item candidate module 105 to determine appropriate media itemcandidates 105.

FIG. 1 also illustrates a media station creation module 114 thatinterprets media station generation rules from media station generationrules database 112 to create media stations using the media itemcandidates 105.

In some embodiments, media station generation rules database 112includes rules or an algorithm for creating a personalized media stationthat is personalized for a specific user or user account. The media itemcandidate module can use the media station generation rules 112 for apersonalized media station to generate a list of candidate media items105. The station creation module 114 can further utilize the mediastation generation rules 112 for a personalized media station to selectfrom the candidate media items to generate a media station that ispersonalized for a specific user or user account.

In some embodiments the media item candidate module 105 can analyze userinformation from user preferences database 104 which can include a listof the media items in the user's personal media library, the user'sratings of and interaction with those media items, user playlists, andother media related information associated with the user to aid indetermining candidates 105.

In some embodiments, station creation module 114 can apply heuristicanalysis to user preferences database 104 and use this analysis toselect one of a plurality of formatted media stations as being mostappropriate for a user's tastes.

In some embodiments, station generation rules 112 can be used by stationcreation module 114 to receive seed media item 113 and select one ormore media items based on that seed. In some embodiments of a seed basedstation, media items having the same or similar genre and/or safe-segueparameters as the seed as the seed can be selected as candidate. In someembodiments, media items that are similar to the seed can be selected ascandidates. In some embodiments, an author or an artist of the seedmedia item can be used to select other media items by the same orsimilar artist(s) or author(s).

In some embodiments, more than one media item can be used as a seed. Insuch embodiments, the characteristics of the multiple media items can beaveraged, for example in the form of a feature vector. The featurevector can then be treated as if it were a single seed media item. Insome embodiments the seed media item can be from a collection of seedmedia items provided by editors or genre experts.

In some embodiments, the station generation rules module 112 can includerules for creating editorially formatted stations.

System 100 further includes station creation module 114. Stationcreation module 114 can be configured to select media items for a mediastation. As illustrated in FIG. 1, station creation module 114 canreceive inputs including station generation rules 112, user preferencesdata 104, and media item candidates 105. Station creation module 114 canselect media items that meet a given station generation rule'sparameters.

System 100 further includes constraints engine 107. Constraints engine107 can include one or more rules that constrain which candidates may beselected, even if a given candidate is otherwise optimal. For example,the constraint can include DCMA rules for the number of times a mediaitem can be played back, or the constraints can include media items notto be played on the media station based on user feedback or media itemanalysis, or constraints can include editorially determined rules. Insome embodiments, the constraints applied by constraints engine 107 candepend on the user's media library. For example, analysis of the user'spersonal library can lead to the discovery that the user does not like80's rock. As a result, a new constraint to avoid 80s rock can beapplied by constraints engine 107 when selecting media items.

As illustrated in FIG. 1, station creation module 114 outputs mediaitems 106 for a media station, which are presented to a user. As theuser experiences media items presented as part of the media station, theuser may provide feedback 108, such as buying the media item, rating ithighly, skipping it, banning it, rating it poorly, tagging a media item,putting the media item in a wish list to purchase later etc. Userfeedback 108, including a listing of all media items presented to theuser can be stored in user preferences/experiences database 104.

FIG. 2 illustrates a further exemplary system configuration. FIG. 2emphasizes portions of the system for which processing is performedoffline and portions of the system which are performed online (i.e., atthe time a media station is requested). Portions of FIG. 2 overlap withFIG. 1, with some components being described with slight variations.Persons of ordinary skill in the art will appreciate that portions ofFIG. 2 can be added to, or substituted for, one or more componentsillustrated in FIG. 1.

FIG. 2, like FIG. 1 illustrates a system in which diverse data sourcescan be used to create a media station. For example both figuresillustrate a source of data that comes from analyses of users libraries(101, 110, 109 of FIGS. 1, and 202, 204, 206, 208, 210, 220 of FIG. 2).Both figures also illustrate a source of data that comes from analysesof media item intrinsic metadata such as genre, title, artist, tempo,beat, origin, etc. (102, 111, 103 of FIGS. 1, and 212, 216, 218, 222 ofFIG. 2). Both figures also illustrate a source of data that comes fromeditorial choices and commercial priorities (generically 115 in FIG. 1,and more specific exemplary databases 224, 225 in FIG. 2). Both figuresalso illustrate one or more components for generating a station (107,112, 114 of FIGS. 1, and 230, 232, and 234 of FIG. 2). While the abovediscussion of this paragraph draws similarities between FIG. 1 and FIG.2 for the benefit of the reader, such discussion should not beconsidered limiting. Additional, different, or alternative componentsmight be considered as part of a data source or components forgenerating a media station. For example, while 236, 238, and 240, couldbe considered components used in generating a media station, they mightalso be components used in selecting similar media items to a seed mediaitem from users' libraries.

Now discussing on FIG. 2 in more detail: FIG. 2 illustrates offlinecomponents for analyzing user's libraries. Each user library is analyzedto determine individual user's preferences, as well as to generate listsof media items determined to be similar to other media items in anindividual user's media library (e.g., through a collaborative filteringprocess—more detail on this process can be found in application Ser. No.12/646,916, filed on Dec. 23, 2009 which is expressly incorporated byreference herein, in its entirety). Specifically FIG. 2 illustrates auser activity database 202 that includes records of an individual user'smedia library activity (e.g., media items purchased, user rating, mediaitems included in a playlist, frequently played, media item experiencehistory, skip count, media item ratings, etc.). User library staticsdatabase 204 includes records of individual user's media items andinformation about those media items. In some embodiments, database 204includes records of a user's experiences with media items whether ontheir personal media devices, or through experiencing media itemspresented by the media station generation module 235. Databases 202 and204 can be analyzed by user activity analyzer 206 and default preferenceanalyzer 208, respectively. Collectively these two analyzer modules 206and 208 serve to analyze the respective data stores 202 and 204 todetermine user preferences 210. For example user activity analyzer 206determines such information as what media items a user experiencesrepeatedly, or has been experiencing a lot recently. In some embodimentsactivity analyzer 206 can also be used to make a determination that auser prefers to experience more new media items as opposed to familiarmedia items, or vice versa, by analyzing user experience data recordedin user activity database 202. Default preference analyzer 208determines library specific information such as the genres that arepredominant in a user's library, favorite artists, seminal genres,seminal artists, etc. In some embodiments, a histogram of seminalartists within the user's seminal genres occurring in the media librarycan be instructive in shaping media item selection and candidates toplay on the media stations. Additional preferences that can bedetermined include a user's tolerance for experience popular media itemsas opposed to a user's preference to be introduced to new media items.Such information is stored in user preference database 210. For exampleuser preference database 210 can include records such as a listing ofall media items similar to each media item in a user's media library,favorite genres favorite media items, favorite artists, disliked mediaitems, current favorites, etc. Such analysis and records are ultimatelycreated for each user offline and are updated periodically to take intoaccount changes in library characteristics and current experiencehabits.

In some embodiments, user activity analyzer can be useful indisambiguating multiple users using a single account by identifyingbreaks in usage patterns, demographics, and genre affinity etc. In someembodiments, the user activity analyzer can even analyze applicationsdownloaded by a user device which can further be used to identifymultiple users on a single account. In such embodiments, the mediastation generation system can attempt to identify which of the multipleusers is presently using the account use only preferences specific forthat user of the account.

In addition to the components of FIG. 2 that perform an analysis ofusers libraries, FIG. 2 also includes offline processing of attributesintrinsic to media items themselves. Metadata database 212 includes dataabout media items. Metadata can be from one or more sources, andincludes data on attributes intrinsic to media items. By intrinsic tomedia items it is meant that the data describes the media item itself.Specifically not included in the term intrinsic attributes is data thatdescribes a user's subjective view of a media item. For example,intrinsic data can include a title, artist, collection, album, genre,tempo, beat, origin (i.e., geographic roots of artist), publisher, orother characteristic of the media item itself. Data that is notintrinsic data includes user ratings, skip counts, energy, bans, likes,tags, etc.

Online store database 214 can further include an additional source ofmetadata regarding a media item. This database also includes data thatis descriptive of the media item, but may also include editorialmetadata. For example online store editors may curate data thatindicates if a media item is associated with a particular era,sub-genre, popularity of the media item, etc. In some embodiments, theonline store database 214 can also include purchase histories andexperience histories of media items based on the aggregated dataobserved from transactions in the online store. Such data can be used tomake fine distinctions between media items and their similarity. Forexample, an analysis of purchase data might reveal that users' that buysongs from an artist in the 70's might not continue to buy songs fromthe same artist published in the 90's. Thus, this data could indicatethat songs by this artist from the 70's and 90's might not be verysimilar even though they are by the same artist. It will be appreciatedthat one or more data items in databases 212 and 214 could beoverlapping. In some embodiments, databases 212 and 214 could be thesame database.

Such intrinsic attributes and editorial attributes can be used byclustering and similarity module 217 to represent each media item as avector. In some embodiments media items can be represented asmulti-dimensional vectors by taking into account many attributes of themedia item. The vectors can be mathematically processed through a knownclustering technique such as locality sensitive hashing, or otheralgorithmic clustering technique to group similar media items together.In some embodiments the clustering technique can be a k-means clusteringanalysis. Such technique is described in greater detail application Ser.No. 12/646,916, filed on Dec. 23, 2009 which is expressly incorporatedby reference herein, in its entirety.

In some embodiments the clustering can be performed by a technique knownin the art as locality sensitive hashing whereby the vectors areanalyzed to create a signature matrix. A signature matrix can be aconversion of the vectors as represented in a highly multi-dimensionalspace into a digital representation of binary 1's and 0's reflecting thepresence or absence of a given attribute. The media items, now reflectedin the signature matrix can be hashed using a collision hashingalgorithm that can hash similar input items into the same bucket. Thesebuckets can then be mapped or at least used to measure distances betweenthe media items grouped within the buckets. The angular distance betweeneach media item, or each bucket, can be used as a measure of similaritybetween the media items or buckets.

FIG. 3 illustrates an exemplary method of performing a clusteringanalysis. As illustrated two possible data input flows 302, 310 can beused (separately or in combination), as inputs into the clusteringprocess. Data flow 302 includes metadata 304 describing characteristicsof media items, while data flow 310 includes purchase history datadescribing how often media items are purchased together or listened toin sequence or are purchased by the same user.

With respect to data flow 302 metadata may need to be “cleaned up” 306so that minor variations in meta-data are eliminated. Such variations inmetadata can be the result of data being derived from more than onesource, or from some attributes that can be represented in more than oneway (e.g. 2 Pac, 2 Pac, 2-Pac). Once the metadata has be cleaned 306, itcan be used to represent a media item as a vector 308.

With respect to data flow 310, purchase history data can be used todetermine how often purchased media items co-occur in the sametransaction or in multiple user's media libraries in a process that issimilar to the collaborative filter similarity process performed onmedia items a user has in its media library as described herein. Suchprocessing can result in the generation of a co-occurrence matrix 314.The co-occurrence matrix data can be normalized and represented as avector 316.

In some embodiments only one data flow 302, 310 is used. In suchembodiments, the vector concatenation process 318 is unnecessary and isskipped. However, when multiple data flows 302, 310 are used, thevectors representing media items output from each process must becombined in a vector concatenation process 318. The vectors can bemathematically combined to generate a new vector to represent each mediaitem and input into the data clustering process 320.

The vectors output from media flows 302, 310, or 318 are highlydimensional. The vectors can be simplified though generation of asignature matrix 322. A signature matrix can be a conversion of thevectors as represented in a highly multi-dimensional space into adigital representation of binary 1's and 0's reflecting the presence orabsence of a given attribute. The media items, now reflected in thesignature matrix can be clustered 324 using a collision hashingalgorithm that can hash similar input items into the same bucket. Thesebuckets can then be mapped 330 or at least used to measure distancesbetween the media items grouped within the buckets. The angular distancebetween each media item, or each bucket, can be used as a measure ofsimilarity between the media items or buckets.

The clustering analysis can be used, for example, to determine genresand sub-genres that are similar and conversely which sub-genres are notsimilar. The clustering analysis can also be used to determine whichmedia items are similar to other media items. Similarity anddissimilarity of clusters or media items can be determined by measuringhow close or far two items map in a coordinate space or by representingclusters or media items as vectors and measuring the distance betweenthe representative vectors. For example, each cluster can be mapped intoa representative feature vector, and clusters that are within adetermined distance of one another can be considered similar, whileclusters that are greater than a determined distance of one another canbe considered dissimilar. Likewise, by converting a cluster into afeature vector, it allows individual media items to be compared directlywith the cluster to determine how similar an individual media item mightbe to a given cluster.

In some embodiments, the clustering analysis can be used to determinebroad preferences within a media library. Many users have media itempreferences that extend into multiple genres or other classifications.Clustering analysis can be useful to determine these preferences. Forexample, a user's music taste might include Alternative Rock (or somesub-genre, thereof), and Classical Music (or some sub-genre, thereof).By performing a clustering analysis on a user's library it can bedetermined that the user has tastes in these two genres, even thoughthese genre's are not themselves similar. This information can be usedto select media items that match the user's taste in Alternative Rockwhen the user is listening to a media station appropriate for such genreselections. By recognizing these different clusters representing auser's taste, it is possible to more exactly match selected media itemsto the user's preferences. If the user's multiple listening preferenceswere not identified and instead the user were considered to have justone musical preference, that musical preference might not berepresentative of the user's actual preferences. For example if theuser's preference for Alternative Rock and Classical music resulting ina single representation of a user's taste, the result might be that thesystem would consider the user to enjoy Alternative Rock backed withsymphony orchestras by blending the users divergent listeningpreferences. This might not actually reflect the user's preferences.

Clustering can also be used to determine a user's preferences in othermedia item attributes. For example, a clustering analysis of a musiclibrary might look like a user has a wide range in music listeningpreferences that spans Rock, Classic Rock, and Folk Rock. However, aclustering analysis might reveal that the user's preference is notrelated to the genre but rather the artist (e.g., Neil Young has albumin each of the three genre's). Thus a feature vector can be created thatrepresents the user's interest in Neil Young music and candidates can beselected by comparison to this feature vector, thus artists more likeNeil Young would be more likely to be selected.

The above implementations of clustering analysis are merelyrepresentative. Clustering analysis could reveal that the user has apreference for any number of media item attributes. Such interests mightneed to be combined to result in a feature vector that is representativeof the user's tastes, or multiple discrete feature vectors, each onebeing an appropriate measure of the user's preferences in the rightcircumstances.

Returning to FIG. 2, the results of the analysis performed by theclustering and similarity module 216 can be stored in feature vectordatabase 218.

The above discussion describes the offline processing that is ultimatelyused to pre-compute databases 210 and 218 prior to their use in creatinga media station. Database 210 includes media items owned by individualusers and other media items that are deemed similar to media items theuser owns. Database 210 also includes additional user preferences suchas media item ratings, likes and dislikes, etc. Database 218 includesdata regarding genres that are deemed similar and not similar. Database218 also includes data regarding similarity of individual media items.The data stored in databases 210 and 218 are then utilized in the onlineprocessing portion of the system illustrated in FIG. 2. As addressedabove, by “online” portion of the system it is meant that this portionof the system can be performed when a media station is requested—ondemand, while the “offline” portion of the system is processed atregular intervals not related to a specific request for a media station.However, the labeling of one ore more features as online or offline ismerely meant to be descriptive of one possible embodiment and notintended to be limiting. It will be appreciated by persons of ordinaryskill in the art that any of the processing performed by the componentsof illustrated in FIG. 2 could be processed online or offline and stillcarry out the present technology.

When a media station request is received by the system illustrated inFIG. 2, rules governing the format of the requested station areretrieved from station generation rules database 242 by media stationgeneration module 235. In some embodiments, media station generationrules can define formatted media stations based on a programming model,personalized stations, seed-based stations, and even sponsored stations.Each station generation rule will include one or more rules forselecting candidates and scoring candidates for a media station.Depending on the media station generation rule, media station generationmodule 235 will request a candidate meeting certain criteria from thecandidate source API 228. The criteria for a candidate can be expressedin the specific interface used to communicate through the candidatesource API 228. For example, the media station generation rule mightdesignate a that a candidates should meet a “library song” criteria, a“complete my album” criteria, a similarity to a named seed trackcriteria, or other criteria by requesting such candidates usinginterfaces defined by the candidate source API 228. Some potential ruleswill be addressed in more detail below.

The request for candidates is ultimately fulfilled by databases 220,224, 226, and 228. It will be appreciated by those of ordinary skill inthe art that there can be more databases as needed for additionalcandidate selection criteria, or that the databases can actually be partof a single database.

The databases 220, 224, 226, and 228 can be populated from the offlinedatabases 210 and 218, or other sources of data. For example userpreference database 210 includes similarity data specifying media itemsin the online store that are similar to media items in a user's librarybased on a collaborative filtering analysis which is the sameinformation that is stored in collaborative filter similarity database220. Similarly, feature vector database 218 can be used to populatefeature vector similarity database 222. Database 224 and 226 can bedatabases of editorially selected songs, or algorithmically collecteddatabases associated with an online store. As such these databases canbe populated from data in the online store database 214. These databasesare presented merely as exemplary databases. Likely many other databaseswill exist.

In some embodiments, the databases can be genre specific or mediastation specific. Table 1 lists exemplary contents of three differentmedia station specific databases for the media station “Alternative.”The databases listed in Table 1 include “Alternative Seminal Artists”listing seminal artists for the Alternative station, “AlternativeCritical Picks” listing editors picks for inclusion in the Alternativestation, and “Alternative Core Heat Seekers” listing editor picks of newmedia items that are not yet charting on the Top 200 media items, orother configurable limit, of that type that may be label or programmingpriorities.

TABLE 1 Alternative Seminal Artists Alternative Critical PicksAlternative Core Heat Seekers Neon Trees The Calendar Hung Itself - TheFeatures - How It Starts - Bright Eyes - Fevers and Mirrors WildernessFUN. One Chance - Omerta - One The Temper Trap - Need Your Chance -Single Love - The Temper Trap Of Monsters and Men Internalize - TallBirds - King Tuff - Keep On Movin' - Internalize/The Sky Is Falling -King Tuff (Bonus Track Version) Single Best Coast Violent Men - Marion -Violent Redd Kross - Researching the Men - EP Blues - Researching theBlues - Single Death Cab For Cutie The Empress - Brett Anderson -Husky - The Woods - Forever Wilderness (Bonus Version) So Lana Del RayGeraldine - Glasvegas - Glasvegas The Walkmen - Love Is Luck - HeavenGotye Daddy's Gone - Glasvegas - Jukebox the Ghost - Somebody -Glasvegas Safe Travels (Bonus Track Version) Broken Bells Mr.Hurricane - Beast - Mr. CocoRosie - We Are On Fire- Hurricane - SingleWe Are On Fire/Tears for Animals Coldplay Evacuate - The Boxer -Rebellion - Friends - Friend Crush - Evacuate - Single of the WeekManifest! Real Estate Semi Automatic - The Boxer Yeasayer - Henrietta -Henrietta - Rebellion - Union Single Foster the People Watermelon - TheBoxer A Silent Film - Danny, Dakota Rebellion - Exits & The WishingWell - Sand & Snow AWOLNATION Plans - The Christophers - Plans - TheHives - Go Right Ahead - EP Lex Hives (Deluxe Edition) Gaz Coombs Don'tTalk In Your Sleep - Ladyhawke - Girl Like Me - Magik Markers - BalfQuarry Anxiety Michael Franti Scanners - Vib Gyor - We Are Liars - No. 1Against the Rush - Not An Island Wixiw The Big Pink Red Lights - VibGyor - We Are Grass Widow - Under the Not An Island Atmosphere -Internal Logic Florence and the Machine Tiny Daggers - Vib Gyor - We TheTemper Trap - Trembling Are Not An Island Hands - The Temper Trap BlackKeys Church Bell - Vib Gyor - We Anna Ternheim - The Longer the Are NotAn Island Waiting (The Sweeter the Kiss) - The Night Visitor Jack WhiteGhosts - Vib Gyor - We Are Not Sigur Rós - Dau.alogn - Valtari An IslandAlex Clare Ultimatum - Vib Gyor - We Are Husky - Tidal Wave - ForeverNot An Island So AWOLNATION Rhombus Suit - Vib Gyor - We The DeerTracks - W - W - Are Not An Island Single . . . . . . . . .

Once the candidates are retrieved by the media station generation module235, the candidates can be scored according to the media stationgeneration rule by candidate scoring module 230. Candidates are scoredaccording to their appropriateness for a given media station or slot inthe media station. In addition to candidate scoring, some candidatesneed to be filtered by filtering module 232 according to one or moreconstraints.

Constraints can be used to limit what media items can be played on agiven media station. In some embodiments the constraints are complianceconstraints, such as those to comply with licensing terms or legalrequirements such as the Digital Millennium Copyright Act (DMCA),licensing agreements, and or sponsorship agreements. For example, amedia playback rule based on a legal regulation may prohibit theplayback of more than three songs by an artist in a one-hour timeperiod, or six skips per experience session. In another example, a mediaplayback rule based on a legal regulation may prohibit the playback ofmore than three songs by an artist within a sequence of 15 songs. In afurther example, a media playback rule based on a licensing agreementmay relax the requirements of a legal regulation and allow the playbackof at most 5 songs by an artist covered by the licensing agreement in aone-hour time period. In yet another example, a media station sponsoredby a particular record label may define a media playback rule thatcompletely prohibits playback of media items from a competitor recordlabel or limits the number of media items from the competitor recordlabel during a specified time period, such as one-hour. In still afurther example, a media playback rule can be defined to limit thenumber of media items skipped by a user in a time interval, such asthree skips in a one-hour time period.

In some embodiments a constraint can be based on user preferences oractions, such as user specified likes or dislikes, or even parentalcontrol preferences.

In some embodiments constraints can include editorial constraints. As isparticularly true with media items, there can be a lot of diversitywithin genres such that some sub-genres don't mix well with othersub-genres. Editors can create a listing of sub-genre's that don't mixwell. Such an exemplary listing is presented in Table 2, below. In theexample presented in Table 2, an editor has noted a collection ofsub-genre's that are not good matches with the basic alternative rockgenre. As such a constraint will eliminate any song media item frombeing included in a media station based on the basic alternative rockgenre if it is of a genre identified in Table 2.

TABLE 2 Alternative Unsafe Segue Sub-Genres International Punk OldSchool Industrial Noise Funk Metal Stoner Rock Grunge Alternative RockSinger-Songwriter Two-Tone Ska Revival Ska Punk New Wave Rock New WavePop Original New Wave Scene Original Post-Punk Adult Alternative NewWave General Industrial Adult Alternative Pop Adult Alternative RockAlternative Country Christian Punk Funk Metal . . .

To apply some media playback rules, media playback historical data maybe required. In some cases, media playback historical data can bemaintained for only as long as is required to ensure that one or moremedia playback rules and/or any other rules are satisfied. For example,for a time based media playback rule, media playback historical data canbe maintained for only as long as the longest time period, such asone-hour. In another example, for a media item sequence based mediaplayback rule, media playback historical data can be maintained for onlyas long as the longest sequence length.

In addition to constraints/filtering module 232 and candidate scoringmodule 230, shuffling module 234 can be used to shuffle the order ofcandidates so that the same candidates are not selected every time andso that they do not always play in the same order.

Also illustrated in FIG. 2 is the user data aggregator module 236. Thismodule aggregates data from an individual user's library, listeninghabits, and store purchases. Such data can be aggregated from one ormore of the offline databases. The data from the user data aggregatormodule 236 can also inform the candidate scoring module to ensure thateven media stations being created from formatted media stations based ona programming model or sponsored stations can be personalized for eachindividual user. Even sponsored stations that are provided by anadvertiser to promote a product or media item can be configured to bepersonalized according to a user's taste. In one embodiment, a mediastation configured to play hit songs to a user will play different hitsongs according to the tastes of each user that listens to the sponsoredstation.

FIG. 2 also illustrates a seed media item 240 being fed into the seedanalyzer module 238 to be used for media stations created by taking intoaccount one or more seed media items. Selected seed media items 240 arealso relevant to user data as preferences since they are media itemsliked enough to be used as a seed.

As noted above, there are several types of media stations, and one ofwhich is a formatted media station based on a programming model. FIG. 4Aillustrates an exemplary programming model for a formatted media stationof audio media items. As shown, media station 400 includes multipleformat rules. Each format rule can be used to identify media itemcandidates that can used to create the media station. For example, aprocessor can execute a format rule to identify one or more media itemsfrom a media item database. These media items can be candidates forplayback on the media station. In some embodiments, a media station caninclude an ordered list of slots where each slot is configured to storea format rule. Creation of the media station can involve iteratingthrough the list of slots and applying the rules assigned to that slot.When playback advances to a given slot, format rule associated with theslot can be executed and a media item from the media item database canbe selected.

As addressed above, a format rule can be used to select and/or weightcandidate media items. Media items having a weighting above a cut offcould be used in media station at the assigned slot. In some embodimentsa media selection rule can be unique to a specific station. FIG. 4Aillustrates exemplary formatting rules in a specified order as part of amedia station programming model for creating a classic rock focusedmedia station. Each format rule is assigned to a slot in the mediastation where during playback of the media station, media issequentially played back according to the slot position. For example,the “Essentials Song Collection [Classic Rock]” format rule can beassigned to the first slot, followed the “Essentials Song Collection['80s Rock]” format rule in the next slot. Each format rule can beconfigured to select a song that meets certain criteria. The “EssentialsSong Collection [Classic Rock]” format rule is configured to select fromthe songs deemed essential in the classic rock category by editors ofthe online store while the “Essentials Song Collection ['80s Rock]”format rule is configured to select from the songs deemed essential inthe '80s rock category by editors of the online store. Since a categoryof song or a song matching particular criteria is selected for each slotduring playback, multiple playbacks of the media station can result indifferent songs being played back. When the song in the 18^(th) slot isplayed and no more slots remain, the media station can loop back to thefirst slot. During a second pass, the format rules can be applied again,thus resulting in the likely possibility of different media items beingselected based on the format rule.

Depending on the configuration of the system, a selected of a media itemcan be performed from media stored on a media playback device or on aserver. In one example, the media playback device selects a song forplayback based on a format rule and subsequently requests the song fromthe server. In another example, the media playback device stores apredetermined number of media items that satisfy the format rulecriteria and selects a media item to playback from the stored mediaitems when a song associated with the format rule is scheduled forplayback. In yet another example, the server can select a media item forplayback that satisfies the criteria of the format rule and cansubsequently transmit the song to the media playback device.

FIG. 4B illustrates an exemplary list of media items (songs in thisexample) that are scheduled to be played on a media station based on theprogramming model illustrated in FIG. 4A. In some embodiments, thescheduled list of media songs can presented to the user so that the useris aware of upcoming tracks, but more commonly, due to licensingrestrictions, the upcoming tracks are not presented to the user (forexample, the DCMA does not permit upcoming songs to be displayed to auser). In some embodiments, the current track and a predetermined numberof upcoming tracks are presented to the user. For example, informationrelated to the currently playing song and the next song schedule to beplayed can be presented to the user.

Formatted media stations can have any number of focuses. Some examplesinclude topical stations (e.g. Christmas holiday station, a Valentine'sDay station, and an Olympics station), sponsored stations, genre basedstations, and editorially created stations. Each station can have acollection of format rules chosen by a station creator to emphasis thestation's focus.

In addition to formatted media stations created based on a specifiedprogramming model, media stations can also be generated based on seedsong specified by a user. Additionally, personalized media stations canbe made that are specific to a user's tastes as determined from ananalysis of the user's library and listening habits. For example, amedia station can be created by analyzing user information that isalready available to the content distribution service. Therefore, apersonalized media station can be created without having the userprovide a seed (e.g., user selected song, artist, genre, or othercharacteristic of music that is used to initially set up a station) or aparameter to create the station. In one example, an automaticallygenerated media station can be preferred over a media station generatedfrom a single seed. A media station that is generated based on a seedmedia item only has a single point of reference to the user's tastes andpreferences. In contrast, a media station that is generated based on theavailable user information can have multiple points of reference to theuser's tastes and preferences and media playback history. The multipledata points can be synthesized into a vector and compared against theresults of the clustering analysis addressed above to determine mediaitems that are closely related to the user's taste.

In some embodiments, the multiple data points can include arepresentation of the user's experience habits over time. It can beinferred that the most recent media experience history more accuratelyrepresents the user's current preferences as well as trends in recentlistening and purchasing. In such embodiments, the user's more recentmedia experience data can be more heavily weighted in determining auser's preferences, and be extension more heavily weighted indetermining media items appropriate for the user's taste. It will beappreciated that a user's taste will vary over time as well as betweenspecific genres or styles of music.

As noted, a media selection rule can define which media items arecandidates for playback on a particular media station. A media selectionrule can also define which media items are not to be played on aparticular station. A media selection rule can also be defined based onone or more general constraints or predefined criteria, such as genre,artist, album, record label or commercial priority. Additionally, amedia selection rule can be defined in terms of a seed item, such as amedia item, album, genre, or artist. A media selection rule based on aseed media item can also include a measure of similarity. By applying aseed based media selection rule, media items determined to besufficiently similar to the seed item can be selected as a candidate forplayback on the media station. Furthermore, a media selection rule canbe defined based on user characteristics, location information, ordemographic information. For example, a media selection rule can specifythat media should be chosen that is popular for users matching the usercharacteristic values of “male,” and “ages 18-22.” In some cases, thisdetermination can be based on metadata associated with the media items.A media selection rule can also be based on user preferences, such asuser specified likes or dislikes, or even parental control preferences.

As is apparent from the above, media stations are very customized toeach specific user even though each media station is generated from thesame media selection rules organized into the same sequence based on thestation format. As such, if a first user were to refer a second user toa media station, the second user would experience a media station verydifferent than the first user because each would be experiencing thestation according to their own preferences. However, in someembodiments, a user may share their own version of a media station withanother user. In such embodiments, the first user can share her versionof the media station with the second user. The second user will thenexperience the media station according to the first user's preferences.It is not generally possible to merely send the same exact collection ofmedia items to a second user without purchasing or gifting the mediaitems due to licensing restrictions.

Media selection rules can be generally grouped into rules that aredesigned to select popular media that matches a user's taste, new mediathat matches a user's taste, best selling media that matches a user'staste. In such instances media selection rules are designed to selectnew or popular media items that matches a user's taste for presentationto a user. Media selection rules can also be driven by an objective ofselling or recommending media items to a user. For example, mediaselection rules might select media items that a user is most likely topurchase. Media selection rules can also be driven by an objective ofexposing a user to media that is currently being promoted based oncommercial priorities and/or relevant to consumers in the culture at anygiven time. Other media selection rules can be driven by a cost savingobjective (cost savings to the media service) by selecting media items auser already owns and already has the rights to listen to on a mediaplayback device. Other media selection rules can be driven by anobjective of playing media items already familiar to a user by selectingmedia items a user already owns or has already experienced. Some mediaselection rules can be configured to select a media item that will gowell with other media items in a media station by selecting media itemsthat are similar, match genre requirements, tempo requirements, etc. Awell selected collection of media selection rules should take intoaccount a user's preferences as well as one or more business goals suchas selling media items or promoting media items.

While media selection rules can come in many forms, some exemplary mediaselection rules include the following:

HEAT-SEEKER: A pool of new media items that are not yet charting on theTop 200 media items of that type that may be label or programmingpriorities and will add fresher content to media stations. In someembodiments, safe segue genres can be ignored.

LISTENERS ALSO BOUGHT: Similarity data that takes into account userpurchases in addition to other similarity metrics, such as media itemco-occurrence.

LISTENERS ALSO LISTENED TO: Similarity data that takes into account userexperiences in addition to other similarity metrics, such as media itemco-occurrence.

CRITICAL PICK: A pool of media items per top level genre or sub genrethat helps to infuse the online store's editorial voice intostations—critically acclaimed, deeper cuts, can cover media items thatmay be missed by the Heat-Seeker media selection rule. In someembodiments, safe segue genres can be ignored.

COMPLETE MY ALBUM: Selects the next best selling track the user does notown from an album the user does not own within the a list of safe seguegenres. In some embodiments, the Complete My Album media selection ruleranks the albums by the number of songs the user already owns to favorconverting album sales.

SELECT FROM [CATEGORY/COLLECTION]: In some embodiments, collection ofmedia items may be organized during the offline processing process. Forexample, editors may have designated a collection of key tracks forcertain genres, or moods, or editors may have otherwise curated acollection of media items that can be utilized as a pool from whichmedia items may be selected.

SALES LEADER: Selects media items within a specified genre the onlinestore with a top sales ranking. If the media rule calls for media itemsfrom a specified period, sales leader may be selected from historicaltrade/industry sales data. Only select media items matching safe seguegenres. In some embodiments, prefer media items released more recently.In some embodiments, user media item similarity data can be used tonarrow selection.

LIBRARY SONG: Selects the highest play count & recently played song in aspecified genre or safe segue genre from the User's MediaLibrary/Purchase History when the candidate song is deemed appropriatefor the media station. In some embodiments, prefer media items releasedin the last 12 months.

In some embodiments, any media selection rule can be sub-ordinate to, orhave one or more other rules sub ordinate to it. For example, if theHeat-Seeker media item selection rule is used to select a media item,the Best Version Media Selection rule can be used to select a versionthat the user is most likely to favor.

In some embodiments, if a media selection rule is unable to provide auseable candidate, a fallback rule can be used to select, for example, aCritical Pick or other editorially-derived media item candidate.

SAFE SEGUE SUB-GENRES/CORE SUB-GENRES: As is particularly true withmedia items, there can be a lot of diversity within genres such thatsome sub-genres don't mix well with other sub-genres. These mediaselection rules select media items from an listing of sub-genre that isdetermined to be safe, or core (substantially related) by editors. Table3 presents an exemplarily listing of safe and core sub-genres for the“Alternative” media station, which is based on alternative rock.

TABLE 3 Alternative Guard Rail/ Safe Segue Sub-Genres Alternative CoreSub-Genres Alternative Pop General Alternative Rock Alternative FolkAlternative Pop Singer-Songwriter Alternative Country Alternative DanceMath Rock Alternative Guard General Indie Pop General Indie Rock RockSinger-Songwriter Post Punk Revival Alternative Female Prog RockAlternative Folk Other Alternative Hip-Hop/Rap Indie Cabaret Pop-RockLo-Fi Neo Glam Neo Prog Rock Neo-Psychedelic Post-Modern Art MusicPost-Rock Slowcore Punk Pop . . .

BEST VERSION: For media items having several versions, this media itemselection rule selects a version of a media from the several versionsthat a user is most likely to favor. A user may be more likely to favorone media item version over another because of an observed preference,such as for live tracks, or studio recorded tracks, or my favor onemedia item version because it fits better with other media items in themedia station. Additionally, some versions of media items might crossgenres, such as with remixes of audio tracks, and a user might bedetermined to appreciate one genre more than the other.

It will be appreciated that the specific media selection rulesreferenced herein are merely exemplary. The media selection rulesreferenced herein can have other or different criteria than that listedherein. Likewise other or different media selection rules are alsopossible and within the scope of the present technology.

Integration between an online store, user's personal media statistics,and a media station creation system in the manner described aboveenables the creation of superior media stations compared to thoseavailable today. But such integration can be cyclical such thatadditional data generated from the media station creation system can beused to refine data (feature vectors) that describe the user's mediapreferences, and especially the user's current listening preferences,which can then be used to create even better media stations.Additionally data generated from the media station creation system canbe utilized by the online store to provide a better experience to theuser there as well. For example, a media item that was recentlyexperienced by the user while experiencing a media station generated bythe media station generation system can be promoted to the user in theonline store. This can be especially helpful in situations wherein theuser rated the media item highly, explicitly tagged the media item, putthe media item in their wishlist, or implicitly suggested interest inconsumption through repeated exposure to the media item without skips orbans, and therefore might be interested in purchasing the media item.

FIG. 5 illustrates a Reflexive Algorithmic Assisted & Heuristics-Driven(RAAHD) Media Station Generation Engine. The system illustrated in FIG.5 takes into account the interaction between store 502, user activityand user media library 503, 503, and media station generation module 506by having each influence the other dynamically. The system can have thedata and insights to operate an online store which dynamically adapts tothe media station experience habits of the user. For example,“Listener's Also Bought” data generated from analysis of a population ofusers' library statistics 505 and stored in a media item candidate pool504 such as pool 1, can be co-mingles that with “Listener's Heard onMedia Station” data in pool n and personalize that further based on thespecific users. Factors such as recency, pattern matching thatpredictively leads to a sale, analyzing the number of impressions andeven the timing of those impressions all become possible once theenvironments are integrated to work reflexively or even predictively.

More specifically FIG. 5 illustrates that data from the online store 502can be processed to create one or more pools of media item candidates504. Furthermore, media item pools can be used by online store to selectmedia items to promote to a user in the online store. For example, asaddressed above, data from the online store 502 can be used to generatea pool of media items that other users also bought in combination withmedia items already contained in the user's media library. This pool canbe used to select a media item(s) to promote in the online store.Likewise the same pool of media items that users also bought can be usedby media station generation module 506 to select a candidate media itemfor potential inclusion in a media station.

In another example, media items presented to a user by media stationgeneration module 506 can be stored in a pool 504 of recently playedmedia items and recorded in the user activity database 503. Such poolcan be used by the online store 502 to select media items to promote tothe user. As media items are purchased by the user, data user librarystatistics database 505 can be updated resulting in updates to the mediaitem candidate pools 504, which in turn refines media items selected bymedia station generation module 506.

FIG. 6 illustrates an exemplary media station user interface. Userinterface 600 can be configured to present available media stations on aclient device. Selection of a media station from user interface 600 canresult in the processing of the media station to identify appropriatecontent to be played. In one example, identified content can meet formatrules of the media station. As shown, user interface 600 includes twosections: a business media station section and a user media stationsection. The business media station section can be configured to presentbusiness media stations such as editorial and sponsored stations. Incontrast, the user media station section can be configured to presentuser media stations that have been personalized for the user. Businessmedia station section 610 includes stations 612, 614, and 616. Usermedia station section includes stations 622 and 624.

As shown, some business media stations can include an icon. For example,station 612 includes icon 613 while station 614 includes icon 615.Station 616 does not have an icon. When the icon is selected, thebusiness media station associated with the icon can be converted into auser media station. Conversion from a business media station to a usermedia station can include changing the properties and attributes of themedia station. For example, a business media station can select mediaaccording to different rules than a user media station. As anotherexample, a business media station can have access to different userinformation than a user media station. For instance, a user mediastation can select media items for playback according to the userlibrary metadata such as the user's music ratings but not the user'sbusiness information such as the user's billing information. Theconversion can result in the addition of a new user media station thatappears in the user media station section 620. In some examples, thebusiness media station may disappear from business media station 610. Inother examples, the business media station may remain in the section butthe icon to convert the station disappears.

FIG. 7A, and FIG. 7B illustrate exemplary possible system embodiments.The more appropriate embodiment will be apparent to those of ordinaryskill in the art when practicing the present technology. Persons ofordinary skill in the art will also readily appreciate that other systemembodiments are possible.

FIG. 7A illustrates a conventional system bus computing systemarchitecture 700 wherein the components of the system are in electricalcommunication with each other using a bus 705. Exemplary system 700includes a processing unit (CPU or processor) 710 and a system bus 705that couples various system components including the system memory 715,such as read only memory (ROM) 720 and random access memory (RAM) 725,to the processor 710. The system 700 can include a cache of high-speedmemory connected directly with, in close proximity to, or integrated aspart of the processor 710. The system 700 can copy data from the memory715 and/or the storage device 730 to the cache 712 for quick access bythe processor 710. In this way, the cache can provide a performanceboost that avoids processor 710 delays while waiting for data. These andother modules can control or be configured to control the processor 710to perform various actions. Other system memory 715 may be available foruse as well. The memory 715 can include multiple different types ofmemory with different performance characteristics. The processor 710 caninclude any general purpose processor and a hardware module or softwaremodule, such as module 1 732, module 2 734, and module 3 736 stored instorage device 730, configured to control the processor 710 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. The processor 710 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction with the computing device 700, an inputdevice 745 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 735 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems can enable a user to provide multiple types of input tocommunicate with the computing device 700. The communications interface740 can generally govern and manage the user input and system output.There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 730 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 725, read only memory (ROM) 720, andhybrids thereof.

The storage device 730 can include software modules 732, 734, 736 forcontrolling the processor 710. Other hardware or software modules arecontemplated. The storage device 730 can be connected to the system bus705. In one aspect, a hardware module that performs a particularfunction can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 710, bus 705, display 735, and soforth, to carry out the function.

FIG. 7B illustrates a computer system 750 having a chipset architecturethat can be used in executing the described method and generating anddisplaying a graphical user interface (GUI). Computer system 750 is anexample of computer hardware, software, and firmware that can be used toimplement the disclosed technology. System 750 can include a processor755, representative of any number of physically and/or logicallydistinct resources capable of executing software, firmware, and hardwareconfigured to perform identified computations. Processor 755 cancommunicate with a chipset 760 that can control input to and output fromprocessor 755. In this example, chipset 760 outputs information tooutput 765, such as a display, and can read and write information tostorage device 770, which can include magnetic media, and solid statemedia, for example. Chipset 760 can also read data from and write datato RAM 775. A bridge 780 for interfacing with a variety of userinterface components 785 can be provided for interfacing with chipset760. Such user interface components 785 can include a keyboard, amicrophone, touch detection and processing circuitry, a pointing device,such as a mouse, and so on. In general, inputs to system 750 can comefrom any of a variety of sources, machine generated and/or humangenerated.

Chipset 760 can also interface with one or more communication interfaces790 that can have different physical interfaces. Such communicationinterfaces can include interfaces for wired and wireless local areanetworks, for broadband wireless networks, as well as personal areanetworks. Some applications of the methods for generating, displaying,and using the GUI disclosed herein can include receiving ordereddatasets over the physical interface or be generated by the machineitself by processor 755 analyzing data stored in storage 770 or 775.Further, the machine can receive inputs from a user via user interfacecomponents 785 and execute appropriate functions, such as browsingfunctions by interpreting these inputs using processor 755.

It can be appreciated that exemplary systems 700 and 750 can have morethan one processor 710 or be part of a group or cluster of computingdevices networked together to provide greater processing capability.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, and so on. Functionality described herein also can beembodied in peripherals or add-in cards. Such functionality can also beimplemented on a circuit board among different chips or differentprocesses executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

1. A system comprising: a similarity database of media item candidatesstoring candidates that are deemed to be similar based on theirintrinsic attributes; an editorial database of media item candidatesstoring candidates that an editor has approved as candidates; a onlinestore database of media item candidates storing candidates that aretargeted to the user for purchase; a user preferences database storinguser preference criteria; and a media station generation moduleconfigured to create a media station in accordance with a programmingmodel having programming slots and a plurality of media selection rulesrespectively assigned to each programming slot, wherein the plurality ofmedia selection rules select at least one candidate from at least twoof: the similarity database, the editorial database, and the onlinestore database, the media station generation module further includes acandidate scoring module configured to weight candidates according tothe user preference criteria.
 2. The system of claim 1 wherein thesimilarity database is generated from an offline clustering processes.3. The system of claim 1, wherein the candidate scoring module weightscandidates for compliance with the respective media selection rule inaddition to the user preference criteria.
 4. The system of claim 1,wherein user preference criteria includes recent experience data, andthe candidate scoring module gives a higher weight to media items theuser has experienced more recently than other media items.
 5. The systemof claim 1 further comprising a collaborative filter database storing acollection of media items that are deemed similar to media items in theuser's media library, wherein the media items are deemed similar basedan analysis of media items owned by a population of users' medialibraries.
 6. A computer implemented method of generating media stationsfor a particular user by a media station generation system, the methodcomprising: analyzing by a processor, in an offline process, theparticular user's media library and media item experience history togenerate a database of user preferences, and a online store database ofmedia items targeted to the particular user for purchase by an onlinestore, wherein the media item experience history includes data regardingmedia item experience data within the particular user's own library,media items presented to the user by the media station generationsystem, and media items experienced within the online store; analyzingby a processor, in an offline process, media items according to theirintrinsic attributes to determine relative similarities of media itemsto other media items to generate a similarity database of media itemsimilarity data; generating an editorial database of media itemcandidates that an editor has approved as candidates; executing, by aprocessor, in an online process, a media station generation rule tocreate a media station in accordance with a programming model havingprogramming slots and a plurality of media selection rules respectivelyassigned to each programming slot, wherein the plurality of mediaselection rules select at least one candidate from at least two of: thesimilarity database, the editorial database, and the online storedatabase; and weighting candidates according to the user preferencecriteria.
 7. The method of claim 6 wherein the similarity database isgenerated from a clustering processes.
 8. The method of claim 6, whereinthe step of weighting weights candidates for compliance with therespective media selection rule in addition to the user preferencecriteria.
 9. The method of claim 6, wherein the media item experiencehistory includes date regarding of recency of a last experience, and thestep of weighting gives a higher weight to media items the user hasexperienced more recently than other media items.
 10. The method ofclaim 6 further comprising: analyzing by a processor, a population ofuser's ownership of media items to determine how often media itemsco-occur in the population of user's media libraries, and generating acollaborative filter database to store the results of the analysis ofthe population of user' ownership of media items.
 11. Acomputer-implemented method comprising: generating, by a processor,collections of media item candidates wherein at least one of thecollections includes media items that are deemed similar based on theirintrinsic attributes; analyzing, by a processor, data associated with auser profile including data representing media items stored within auser's media library, online store purchase data, and user experiencedata to infer user preference criteria; selecting, by a processor, afirst plurality of media items from the collections of media itemcandidates in accordance with a first media selection rule; andweighting, by a processor, the first plurality of media item candidatesaccording to each candidates' correspondence to one of inferred userpreference criteria.
 12. The method of claim 11, wherein the one of theinferred user preference criteria includes a recent experience criteria,and media items that have been experienced more recently are given ahigher weight.
 13. The method of claim 11, wherein the one of theinferred user preference criteria includes a period preference criteria,and media items being selected based on a media selection ruleconfigured to select the first plurality of media items on the basis oftheir categorization as belonging to a genre are given a higher weightwhen they were published during a preferred period as indicated by theperiod preference criteria.
 14. The method of claim 11, wherein the oneof the inferred user preference criteria includes a period preferencecriteria, and media items being selected based on a media selection ruleconfigured to select the first plurality of media items on the basis ofbeing authored by a specified artist are given a higher weight when theywere published during a preferred period as indicated by the periodpreference criteria.
 15. The method of claim 11, wherein the inferreduser preference criteria includes a media library fit criteria, and therespective ones of the first plurality of media items that most closelymatch the media library fit criteria are given a higher weight.
 17. Themethod of claim 15, wherein the media library fit criteria is determinedby: clustering the items stored within a user's media library;representing clusters having the greatest proportion of media itemscontained therein as a best fit vector, whereby one or more best fitvectors are represent media items that are a best fit for a user'slibrary.
 17. The method of claim 16, wherein it is determined that therespective ones of the first plurality of media items that most closelymatch the media library fit criteria by comparing a vector representingeach of the respective ones of the first plurality of media items withthe one or more best fit vectors, and weighting the media items thathave the smallest distance between the vector representing itself andone of the best fit vectors more highly than those with a largerdistance between the vector representing itself and one of the best fitvectors.
 18. A product comprising: a computer readable medium; and acomputer readable instruction, stored on the computer readable medium,that when executed is effective to cause a computer to: analyze by aprocessor, in an offline process, the particular user's media libraryand media item experience history to generate a database of userpreferences, and a online store database of media items targeted to theparticular user for purchase by an online store, wherein the media itemexperience history includes data regarding media item experience datawithin the particular user's own library, media items presented to theuser by the media station generation system, and media items experiencedwithin the online store; analyze by a processor, in an offline process,media items according to their intrinsic attributes to determinerelative similarities of media items to other media items to generate asimilarity database of media item similarity data; generate an editorialdatabase of media item candidates that an editor has approved ascandidates; execute, by a processor, in an online process, a mediastation generation rule to create a media station in accordance with aprogramming model having programming slots and a plurality of mediaselection rules respectively assigned to each programming slot, whereinthe plurality of media selection rules select at least one candidatefrom at least two of: the similarity database, the editorial database,and the online store database; and weight candidates according to theuser preference criteria.
 19. The product of claim 18 wherein thesimilarity database is generated from a clustering processes.
 20. Theproduct of claim 18, wherein the step of weighting weights candidatesfor compliance with the respective media selection rule in addition tothe user preference criteria.
 21. The product of claim 18, wherein themedia item experience history includes date regarding of recency of alast experience, and the step of weighting gives a higher weight tomedia items the user has experienced more recently than other mediaitems.
 22. The product of claim 18 further comprising: analyzing by aprocessor, a population of user's ownership of media items to determinehow often media items co-occur in the population of user's medialibraries, and generating a collaborative filter database to store theresults of the analysis of the population of user' ownership of mediaitems.